{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import numpy as np\n",
    "import time\n",
    "import math\n",
    "import sys\n",
    "import tensorflow as tf\n",
    "sys.path.append(\"..\") \n",
    "import d2lzh_tensorflow2 as d2l"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch 20, x1 -2.382563, x2 -0.158591\n"
     ]
    },
    {
     "data": {
      "image/png": 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i9Y1BrtnSwjVbjJBcsqGx/vMeHv0E7L4Ltt8Mb/lXk6chCHVEqaTJO0ZI8sUS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\nSFKUaJuO/wAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xc4a7969198>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def adagrad_2d(x1, x2, s1, s2):\n",
    "    g1, g2, eps = 0.2 * x1, 4 * x2, 1e-6  # 前两项为自变量梯度\n",
    "    s1 += g1 ** 2\n",
    "    s2 += g2 ** 2\n",
    "    x1 -= eta / math.sqrt(s1 + eps) * g1\n",
    "    x2 -= eta / math.sqrt(s2 + eps) * g2\n",
    "    return x1, x2, s1, s2\n",
    "\n",
    "def f_2d(x1, x2):\n",
    "    return 0.1 * x1 ** 2 + 2 * x2 ** 2\n",
    "\n",
    "eta = 0.4\n",
    "d2l.show_trace_2d(f_2d, d2l.train_2d(adagrad_2d))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch 20, x1 -0.002295, x2 -0.000000\n"
     ]
    },
    {
     "data": {
      "image/png": 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QhBMgwlHFSzY18+M/uoKL1zXy1/fv4vrPPcLTx0ZqPaylzcH/hnveDasuME+Fj899FgRh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\ngg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xc4a79ead30>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "eta = 2\n",
    "d2l.show_trace_2d(f_2d, d2l.train_2d(adagrad_2d))"
   ]
  },
  {
   "cell_type": "code",
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    "features, labels = d2l.get_data_ch7()\n",
    "\n",
    "def init_adagrad_states():\n",
    "    s_w = tf.zeros((features.shape[1],1),dtype=tf.float32)\n",
    "    s_b = tf.zeros(1,dtype=tf.float32)\n",
    "    return (s_w, s_b)\n",
    "\n",
    "def adagrad(params, states, hyperparams,grads):\n",
    "    eps = 1e-6\n",
    "    i=0\n",
    "    for p, s in zip(params, states):\n",
    "        s += (grads[i]**2)\n",
    "        p.assign_sub(hyperparams['lr']*grads[i]/tf.sqrt(s+eps))\n",
    "        i+=1"
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   "execution_count": 20,
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      "loss: 0.255291, 1.154072 sec per epoch\n"
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   "source": [
    "d2l.train_ch7(adagrad, init_adagrad_states(), {'lr': 0.01}, features, labels)"
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  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from tensorflow.keras import optimizers\n",
    "trainer = optimizers.Adagrad(learning_rate=0.01)"
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  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
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     "text": [
      "loss: 1.511836, 1.368635 sec per epoch\n"
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